Optimal strategies to protect a sub-population at risk due to an established epidemic.
Publication Date
2022-01Journal Title
J R Soc Interface
ISSN
1742-5689
Publisher
The Royal Society
Volume
19
Issue
186
Language
en
Type
Article
This Version
AO
VoR
Metadata
Show full item recordCitation
Bussell, E. H., & Cunniffe, N. (2022). Optimal strategies to protect a sub-population at risk due to an established epidemic.. J R Soc Interface, 19 (186) https://doi.org/10.1098/rsif.2021.0718
Abstract
Epidemics can particularly threaten certain sub-populations. For example, for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the elderly are often preferentially protected. For diseases of plants and animals, certain sub-populations can drive mitigation because they are intrinsically more valuable for ecological, economic, socio-cultural or political reasons. Here, we use optimal control theory to identify strategies to optimally protect a 'high-value' sub-population when there is a limited budget and epidemiological uncertainty. We use protection of the Redwood National Park in California in the face of the large ongoing state-wide epidemic of sudden oak death (caused by Phytophthora ramorum) as a case study. We concentrate on whether control should be focused entirely within the National Park itself, or whether treatment of the growing epidemic in the surrounding 'buffer region' can instead be more profitable. We find that, depending on rates of infection and the size of the ongoing epidemic, focusing control on the high-value region is often optimal. However, priority should sometimes switch from the buffer region to the high-value region only as the local outbreak grows. We characterize how the timing of any switch depends on epidemiological and logistic parameters, and test robustness to systematic misspecification of these factors due to imperfect prior knowledge.
Keywords
Life Sciences–Mathematics interface, Research articles, optimal control theory, sudden oak death, Phytophthora ramorum, policy plot, buffer region, parameter uncertainty
Sponsorship
Biotechnology and Biological Sciences Research Council (1643594)
Identifiers
rsif20210718
External DOI: https://doi.org/10.1098/rsif.2021.0718
This record's URL: https://www.repository.cam.ac.uk/handle/1810/332996
Rights
Licence:
http://creativecommons.org/licenses/by/4.0/
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